Ontotext was one of the first vendors into the graph space and today it markets two offerings: GraphDB, an RDF database, and the Ontotext Platform, a broader offering that targets general-purpose data management and analytics based on the use of knowledge graphs. The company was founded in 2000, in Bulgaria, to investigate semantic technologies. It has recently gone through a (successful) round of growth funding, and although its head office remains on Sofia, it has continued to expand its reach across Europe, the US, and the Asia-Pacific. The company also has offices in New York and Switzerland.
Ontotext partners with various leading IT services providers, a number of which embed GraphDB in their AI platforms. Household names that use Ontotext include the BBC, the Financial Times and Elsevier, S&P Global Platts, amongst others. The company has won funding from the EU for upwards of 30 projects and it is actively engaged in various standards bodies.
Unlike most other vendors in this space the company has developed specific solutions for various industry sectors, including publishing, market intelligence, financial services, life sciences, construction (building management) and healthcare. In addition, knowledge graphs, are a major area of focus for Ontotext and the company sees these (and we agree) as key enablers for general-purpose data management applications such as reference and master data management, metadata-based content management, information and relationship discovery, and content management solutions that involve text analytics on top of (big data) knowledge graphs. In addition, the company sees a major role for knowledge graphs in supporting AI and machine learning in general. It argues that, typically, half of the research required to build training sets for machine learning is thrown away in conventional environments but that by basing the discovery of training data on knowledge graphs you get to retain that research for reuse in subsequent projects.